chapter 1: data storagembm.konkuk.ac.kr/wp-content/uploads/2016/12/ict개론… · ·...

Copyright © 2015 Pearson Education, Inc.

Chapter 1:Data Storage


• 1.1 Bits and Their Storage• 1.2 Main Memory• 1.3 Mass Storage• 1.4 Representing Information as Bit Patterns• 1.5 The Binary System

Chapter 1: Data Storage

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• 1.6 Storing Integers• 1.7 Storing Fractions• 1.8 Data and Programming• 1.9 Data Compression• 1.10 Communications Errors

Chapter 1: Data Storage (continued)

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• Bit: Binary Digit (0 or 1)• Bit Patterns are used to represent information

– Numbers– Text characters– Images– Sound– And others

Bits and Bit Patterns

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• Boolean Operation: An operation that manipulates one or more true/false values

• Specific operations– AND– OR– XOR (exclusive or)– NOT

Boolean Operations

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Figure 1.1 The possible input and output values of Boolean operations AND, OR, and XOR (exclusive or)

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• Gate: A device that computes a Boolean operation– Often implemented as (small) electronic

circuits– Provide the building blocks from which

computers are constructed– VLSI (Very Large Scale Integration)

Gates

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Figure 1.2 A pictorial representation of AND, OR, XOR, and NOT gates as well as their input and output values

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• Flip-flop: A circuit built from gates that can store one bit.– One input line is used to set its stored value to 1– One input line is used to set its stored value to 0– While both input lines are 0, the most recently

stored value is preserved

Flip-flops

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Figure 1.3 A simple flip-flop circuit

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Figure 1.4 Setting the output of a flip-flop to 1

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Figure 1.4 Setting the output of a flip-flop to 1 (continued)

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Figure 1.4 Setting the output of a flip-flop to 1 (continued)

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Figure 1.5 Another way of constructing a flip-flop

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• Hexadecimal notation: A shorthand notation for long bit patterns– Divides a pattern into groups of four bits each– Represents each group by a single symbol

• Example: 10100011 becomes A3

Hexadecimal Notation

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Figure 1.6 The hexadecimal coding system

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• Cell: A unit of main memory (typically 8 bits which is one byte)– Most significant bit: the bit at the left (high-

order) end of the conceptual row of bits in a memory cell

– Least significant bit: the bit at the right (low-order) end of the conceptual row of bits in a memory cell

Main Memory Cells

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Figure 1.7 The organization of a byte-size memory cell

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• Address: A “name” that uniquely identifies one cell in the computer’s main memory– The names are actually numbers.– These numbers are assigned consecutively

starting at zero.– Numbering the cells in this manner associates

an order with the memory cells.

Main Memory Addresses

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Figure 1.8 Memory cells arranged by address

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• Random Access Memory (RAM):Memory in which individual cells can be easily accessed in any order

• Dynamic Memory (DRAM): RAM composed of volatile memory

Memory Terminology

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• Kilobyte: 210 bytes = 1024 bytes– Example: 3 KB = 3 times1024 bytes

• Megabyte: 220 bytes = 1,048,576 bytes– Example: 3 MB = 3 times 1,048,576 bytes

• Gigabyte: 230 bytes = 1,073,741,824 bytes– Example: 3 GB = 3 times 1,073,741,824 bytes

• Terabyte: 240 bytes • Petabyte: 250 bytes• Exabyte: 260 bytes

Measuring Memory Capacity

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• Additional devices:– Magnetic disks– CDs– DVDs

• Advantages over main memory– Less volatility– Larger storage capacities– Low cost– In many cases can be removed

Mass Storage

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– Magnetic tape– Flash drives– Solid-state disks


Figure 1.9 A magnetic disk storage system

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Figure 1.10 CD storage

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• Flash Memory – circuits that traps electrons in tiny silicon dioxide chambers

• Repeated erasing slowly damages the media

• Mass storage of choice for:– Digital cameras

• SD Cards provide GBs of storage

Flash Drives

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– Smartphones


• Each character (letter, punctuation, etc.) is assigned a unique bit pattern.– ASCII: Uses patterns of 7-bits to represent

most symbols used in written English text– ISO developed a number of 8 bit extensions to

ASCII, each designed to accommodate a major language group

– Unicode: Uses patterns up to 21-bits to represent the symbols used in languages world wide, 16-bits for world’s commonly used languages

Representing Text

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Figure 1.11 The message “Hello.” in ASCII or UTF-8 encoding

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• Binary notation: Uses bits to represent a number in base two

• Limitations of computer representations of numeric values– Overflow: occurs when a value is too big to be

represented– Truncation: occurs when a value cannot be

represented accurately

Representing Numeric Values

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• Bit map techniques– Pixel: short for “picture element”– RGB– Luminance and chrominance

• Vector techniques– Scalable– TrueType and PostScript

Representing Images

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• Sampling techniques– Used for high quality recordings– Records actual audio

• MIDI– Used in music synthesizers– Records “musical score”

Representing Sound

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Figure 1.12 The sound wave represented by the sequence 0, 1.5, 2.0, 1.5, 2.0, 3.0, 4.0, 3.0, 0

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The traditional decimal system is based on powers of ten.

The Binary system is based on powers of two.

The Binary System

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Figure 1.13 The base ten and binary systems

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Figure 1.14 Decoding the binary representation 100101

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Figure 1.15 An algorithm for finding the binary representation of a positive integer

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Figure 1.16 Applying the algorithm in Figure 1.15 to obtain the binary representation of thirteen

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Figure 1.17 The binary addition facts

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Figure 1.18 Decoding the binary representation 101.101

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• Two’s complement notation: The most popular means of representing integer values

• Excess notation: Another means of representing integer values

• Both can suffer from overflow errors

Storing Integers

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Figure 1.19 Two’s complement notation systems

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Figure 1.20 Coding the value -6 in two’s complement notation using four bits

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Figure 1.21 Addition problems converted to two’s complement notation

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Figure 1.22 An excess eight conversion table

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Figure 1.23 An excess notation system using bit patterns of length three

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• Floating-point Notation: Consists of a sign bit, a mantissa field, and an exponent field.

• Related topics include– Normalized form– Truncation errors

Storing Fractions

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Figure 1.24 Floating-point notation components

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Figure 1.25 Encoding the value 2 5⁄8

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A programming language is a computer system created to allow humans to precisely express algorithms using a higher level of abstraction.

Data and Programing

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• Python: a popular programming language for applications, scientific computation, and as an introductory language for students

• Freely available from www.python.org• Python is an interpreted language

– Typing:print('Hello, World!')

– Results in:Hello, World!

Getting Started with Python

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• Variables: name values for later use• Analogous to mathematic variables in

algebras = 'Hello, World!'print(s)

my_integer = 5my_floating_point = 26.2my_Boolean = Truemy_string = 'characters'my_integer = 0xFF

Variables

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print(3 + 4) # Prints 7print(5 – 6) # Prints -1print(7 * 8) # Prints 56print(45 / 4) # Prints 11.25print(2 ** 10) # Prints 1024

s = 'hello' + 'world's = s * 4print(s)

Operators and Expressions

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# A converter for currency exchange.

USD_to_GBP = 0.66 # Today's exchange rateGBP_sign = '\u00A3' # Unicode value for £dollars = 1000 # Number dollars to convert

# Conversion calculationspounds = dollars * USD_to_GBP

# Printing the resultsprint('Today, $' + str(dollars)) print('converts to ' + GBP_sign + str(pounds))

Currency Conversion

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• Syntax errorsprint(5 +)SyntaxError: invalid syntax

pront(5)NameError: name 'pront' is not defined

• Semantic errors– Incorrect expressions like

total_pay = 40 + extra_hours * pay_rate

• Runtime errors– Unintentional divide by zero

Debugging

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• Lossy versus lossless• Run-length encoding• Frequency-dependent encoding

(Huffman codes)• Relative encoding (Differential encoding)• Dictionary encoding (Includes adaptive dictionary

encoding such as LZW encoding.)

Data Compression

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• GIF: Good for cartoons• JPEG: Good for photographs• TIFF: Good for image archiving

Compressing Images

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• MPEG– High definition television broadcast– Video conferencing

• MP3– Temporal masking– Frequency masking

Compressing Audio and Video

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• Parity bits (even versus odd)• Checkbytes• Error correcting codes

Communication Errors

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Figure 1.26 The ASCII codes for the letters A and F adjusted for odd parity

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Figure 1.27 An error-correcting code

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Figure 1.28 Decoding the pattern 010100 using the code in Figure 1.27

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End of

Chapter

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